1. Field of the Invention
The present invention relates to a spectrum analysis and a display method of time series data derived from a series of signal issued from a living organism, wherein: in the spectrum analysis method, the series of signals issued from the living organism are sampled in amplitude at predetermined time intervals and converted into digital form through an analog-to-digital conversion processing and the like to obtain a time series data, which data thus obtained is then decomposed into a plurality of frequency components each of which is determined in amplitude; and, in the display method, variations in amplitude of the frequency components of the time series data are transformed into corresponding densities of a predetermined color including a gray color according to a predetermined color scale or a predetermined gray (i.e., white-to-black) scale and, and displayed on a display screen on the basis of a time base of the display screen.
2. Description of the Related Art
Heretofore, in most cases, a conventional spectrum analysis method has been performed through the xe2x80x9cfast Fourier transformation (i.e., FTT)xe2x80x9d processing. Also used is a so-called MEM (i.e., Maximum Entropy Method), which makes it possible to perform a necessary spectrum analysis of time series data even when the time series data is based on a series of signals issued for a relatively short period of time. These spectrum analyses of the time series data are performed at regular intervals of time to obtain necessary results, which are displayed in a three-dimensional data diagram on a display screen.
However, in case that information of the living organism is processed through the fast Fourier transformation processing: the larger the frequency resolution, the slower the spectrum analysis of the time series data will be performed through the fast Fourier transformation processing. Consequently, in case that the fast Fourier transformation processing is used to deal with the time series data varying in a relatively short period of time, when a period of time required to perform such spectrum analysis is reduced, variations in amplitude of the frequency components of the time series data become hard to detect. On the other hand, when the period of time required to perform the spectrum analysis of the time series data is increased to improve, in frequency resolution, the three-dimensional data diagram showing the frequency components of the time series data, variations in amplitude of the frequency components of the time series data become hard to detect.
Further, due to the dispersion in spectrum of the time series data caused by discontinuity appearing in each of the opposite ends of an analysis region characteristic of the Fourier transformation processing, it is hard for the Fourier transformation processing to detect both of: variations in amplitude of the frequency components of the time series data, which variations appear in a relatively short period of time; and, relatively small variations in frequency of the frequency components of the time series data. This is one of drawbacks of the Fourier transformation processing in dealing with the time series data.
In most cases, the signal issued form the living organism varies in a relatively short period of time. Under such circumstances, it is very important to grasp the entire variations in mode of the signal occurring in such a short period of time. However, heretofore, the Fourier transformation processing has been used to grasp variations in frequency of the signal, and, therefore suffered from its relatively long period of analysis time, which makes it impossible to detect the variations in mode of the signal appearing in such a short period of time.
For example, in the case of an electrocardiogram with a frequency resolution of 1 cpm (i.e., cycle per minute), only a result obtained in spectrum analysis of the time series data is an average value (i.e., mean information) of a series of signals issued from a patient""s heart for one minute. However, in general, an abnormal irregular rhythm called xe2x80x9carrhythmiaxe2x80x9d of the patient""s heartbeats has a few seconds"" duration. Consequently, variations in amplitude of frequency resulted from the Fourier transformation processing of such xe2x80x9carrhythmiaxe2x80x9d are extremely small, which makes it impossible to grasp the entire activity mode of the patient""s heart.
On the other hand, in the case of the above-mentioned MEM (i.e., Maximum Entropy Method), a value of amplitude in frequency of the time series data obtained through the MEM is not stable and therefore not reliable. Due to this, there is not recognized any relationship between the distribution in spectrum of the frequency components of the time series data and the time series data itself. Consequently, the MEM is not used in preparation of the electrocardiogram. In the case of the electrocardiogram and like diagrams having been already known in waveform in its meaning in detail, it is considered inadequate to employ the spectrum analysis which is not reliable in amplitude of a weak one of the frequency components of the time series data.
Further, in general, the results of the spectrum analyses periodically performed are represented by using a three-dimensional diagram. However, such a three-dimensional diagram suffers from the disadvantage that the amplitude of the weak one of the frequency components of the time series data is often hidden by a larger amplitude of another frequency component disposed in front of the weak frequency component in the diagram, which poses another disadvantage that it is hard to grasp the entire variations in mode of the frequency components of the time series data varying with elapsed time. Further, it is necessary for a desired display method to enable a doctor to grasp the entire variations in mode of the frequency components of the time series data in a relatively short time and also grasp the amplitude of the weak frequency component of the time series data even when a conventional type of spectrum analysis method is performed to display its results on the display screen by using a color map or diagram.
As described above, the fast Fourier transformation (i.e., FFT) processing and the MEM (i.e., Maximum Entropy Method) both having heretofore been used in the spectrum analysis of the time series data suffer from various problems in dealing with the signals issued from the living organism and like signals varying in a relatively short period of time.
Consequently, it is an object of the present invention to provide a spectrum analysis method and a display method of time series data derived from a series of signals issued from a living organism, which enables a doctor to precisely grasp variations in mode of frequency components of the time series data and also grasp variations in amplitude of even a weak one of the frequency component of the time series data.
In accordance with a first aspect of the present invention, the above object of the present invention is accomplished by providing:
A spectrum analysis method of time series data derived from a series of signals issued from a living organism, the signals being converted into digital form of the time series data, comprising:
a step for decomposing the time series data into a plurality of frequency components by means of a set of band pass filters, wherein the band pass filters have their cut-off frequencies shifted or offset from each other by their passing band widths, respectively, so that amplitude information of the frequency components of the time series data is obtained.
In accordance with a second aspect of the present invention, the above object of the present invention is accomplished by providing:
A display method of time series data derived from a series of signals issued from a living organism, the signals being converted into digital form of the time series data, comprising the steps of:
decomposing the time series data into a plurality of frequency components by means of a set of band pass filters, wherein the band pass filters have their cut-off frequencies shifted or offset from each other by their passing band widths, respectively;
sampling in amplitude the frequency components of the time series data at predetermined time intervals;
converting the thus sampled amplitudes of the frequency components of the time series data into corresponding densities of a predetermined color including a gray color, according to a predetermined gray scale or a predetermined color scale; and
displaying variations in the corresponding densities of the predetermined color including the gray color on a display screen on the basis of a time base of said display screen.
In other words, in order to accomplish the above object, the present invention provides a processing means for dealing with the time series data through the individual digital band pass filters.
In operation, these digital band pass filters decompose the time series data into the plurality of the frequency components.
In this processing means of the present invention: the number of the digital band pass filters is the same as that of the passing band widths; and, the digital band pass filters have their cut-off frequencies shifted or offset from each other by their passing band widths, respectively. In operation, the processing means of the present invention is capable of detecting any variations in amplitude of the frequency components in a relatively short period of time.
Further, since the set of the digital band pass filters having the above construction may continuously carry out the spectrum analysis of the time series data, there is no fear that the processing means of the present invention suffers from any dispersion in spectrum caused by discontinuity appearing in the Fourier transformation processing. Consequently, in the processing means of the present invention, it is possible for the doctor to precisely grasp even a slight variation in amplitude of the frequency components of the time series data, and also possible to precisely grasp any variations occurring in a relatively short period of time in amplitude of the frequency components of the time series data.
A response time required to perform the spectrum analysis of the time series data into the frequency components is dependent on delay times of the digital band pass filters. For example, in the case of the processing means of the present invention using the digital band pass filters each having a passing band width of 1 cpm, a necessary delay time is approximately six seconds, which is only one tenths as long as the conventional analysis using the Fourier transformation processing required. This means that the processing means of the present invention is capable of performing the spectrum analysis of the time series data in a relatively short period of time. Further, in the processing means of the present invention having the above construction: for wider values of the passing band widths of the digital band pass filters used in the processing means, the response time of the processing means becomes shorter in a substantially inverse proportion manner.
In the display method of the present invention: the set of the digital band pass filters are used to decompose the time series data into the frequency components; the thus decomposed frequency components of the time series data are then sampled in amplitude at predetermined time intervals; the thus sampled amplitudes of the frequency components are transformed into the corresponding densities of a predetermined color including a gray color; these corresponding densities of the predetermined color thus obtained are then displayed in a diagram on a display screen on the basis of a time base of the display screen. Consequently, in contrast with the prior art, the display method of the present invention having the above construction is free from a fear that the amplitude of a weak one of the frequency components of the time series data is hidden by the amplitude of another larger frequency component disposed in front of such weak frequency component. Due to the above fact, it is possible for the display method of the present invention to display the entire results of the spectrum analysis of the time series data in the diagram, and thereby enabling the doctor to clearly grasp the entire variations in amplitude of the frequency components of the time series data on the display screen.